Over the last decade, deep learning has made several breakthroughs and demonstrated successful applications in various fields (e.g. in computer vision [1]}, [2]}, [3]}, [4]}, object detection [5]}, or NLP [6]}, [7]}). This success is mainly due to its automation of the feature engineering process. This success is mainly attributable to the fact that it automates the feature engineering process. Rather than manually designed features, features are learned in an end-to-end process from data. The need for improved architecture has swiftly followed the advent of deep learning. Experts now place a premium on architecture engineering in lieu of features engineering.
